Research on path planning of underwater inspection robot based on improved ant colony algorithm

Shen Zhaowen; Pan Xinfeng; Huang Mengshi; Yang Tianci; Zhang Wenze; Yi Zhengyao

doi:10.11993/j.issn.2096-3920.2026-0066

Article Contents

Article Navigation > Journal of Unmanned Undersea Systems > 2026 > Accepted Manuscript

Shen Zhaowen, Pan Xinfeng, Huang Mengshi, Yang Tianci, Zhang Wenze, Yi Zhengyao. Research on path planning of underwater inspection robot based on improved ant colony algorithm[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2026-0066

Citation:

Shen Zhaowen, Pan Xinfeng, Huang Mengshi, Yang Tianci, Zhang Wenze, Yi Zhengyao. Research on path planning of underwater inspection robot based on improved ant colony algorithm[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2026-0066

Citation:

Shen Zhaowen, Pan Xinfeng, Huang Mengshi, Yang Tianci, Zhang Wenze, Yi Zhengyao. Research on path planning of underwater inspection robot based on improved ant colony algorithm[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2026-0066

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Research on path planning of underwater inspection robot based on improved ant colony algorithm

doi: 10.11993/j.issn.2096-3920.2026-0066

1.
Navigation and Engineering College, Dalian Ocean University, Dalian 110623, China
2.
China Shipbuilding Industry Corporation 760 Research Institute, Dalian 116013, China

Received Date: 2026-04-02
Accepted Date: 2026-05-11
Rev Recd Date: 2026-05-04

Available Online: 2026-09-04

Abstract

Abstract

Aiming at the problems of slow convergence speed, easy to fall into local optimum and poor environmental adaptability of traditional ant colony algorithm in path planning of underwater inspection robot, an improved ant colony algorithm suitable for path planning of underwater inspection robot is proposed. Firstly, the ant colony pheromone update strategy is optimized, and a comprehensive evaluation function including path length, safety, smoothness and water flow adaptability is constructed. The pheromone release coefficient is dynamically adjusted by the path fitness. Adjust the pheromone volatilization coefficient to adjust with the iterative process, and speed up the convergence speed in the later stage of the algorithm iteration; the water flow factor and obstacle distance factor are introduced into the heuristic function to guide the ants to choose a better path. Then the simulated annealing mechanism is integrated to avoid the algorithm falling into local optimum. Finally, the B-spline curve is used to smooth the path to reduce the energy consumption of the underwater vehicle. The simulation results show that the improved ant colony algorithm not only has faster convergence speed and fewer turning points, but also significantly shortens the optimal path length.
- Ant colony algorithm,
- simulated annealing mechanism,
- underwater inspection robot,
- path planning

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References(17)

References

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